# HG changeset patch
# User James Bergstra <bergstrj@iro.umontreal.ca>
# Date 1256252967 14400
# Node ID b2948ae5087c1a07c97d57b9aff1e77b9e286a57
# Parent  c19085585464fdddaba7da611400569c31c28baa
added forget() to tearDown of cifar unittests

diff -r c19085585464 -r b2948ae5087c pylearn/dataset_ops/tests/test_cifar10.py
--- a/pylearn/dataset_ops/tests/test_cifar10.py	Thu Oct 22 18:53:50 2009 -0400
+++ b/pylearn/dataset_ops/tests/test_cifar10.py	Thu Oct 22 19:09:27 2009 -0400
@@ -1,121 +1,131 @@
+import unittest
 import numpy
 import theano
 from theano.compile.sandbox import pfunc, shared
 from theano import tensor
 
-from pylearn.dataset_ops.cifar10 import cifar10
-
-def test_single():
-
-    s_idx = theano.tensor.iscalar()
-
-    for dtype in ('uint8', 'float64', 'float32'):
-        x, y = cifar10(s_idx, split='train', dtype=dtype, rasterized=False, color='grey')
-        assert x.dtype == dtype
-
-def test_shape_range():
-    """Test that the image numbers come out in the right range for various dtypes"""
-    s_idx = theano.tensor.iscalar()
+from pylearn.dataset_ops.cifar10 import cifar10, forget
 
-    #uint8 not-rasterized grey
-    x, y = cifar10(s_idx, split='train', dtype='uint8', rasterized=False, color='grey')
-    f = pfunc([s_idx], [x,y])
-    xval, yval = f(0)
-    assert str(xval.dtype) == 'uint8'
-    assert xval.min() >= 0
-    assert xval.max() < 256
-    assert xval.max() > 1
-    assert xval.shape == (32,32)
-
-    #uint8 not-rasterized rgb
-    x, y = cifar10(s_idx, split='train', dtype='uint8', rasterized=False, color='rgb')
-    f = pfunc([s_idx], [x,y])
-    xval, yval = f(0)
-    assert str(xval.dtype) == 'uint8'
-    assert xval.min() >= 0
-    assert xval.max() < 256
-    assert xval.max() > 1
-    assert xval.shape == (32,32, 3)
-
-    #uint8 rasterized grey
-    x, y = cifar10(s_idx, split='train', dtype='uint8', rasterized=True, color='grey')
-    f = pfunc([s_idx], [x,y])
-    xval, yval = f(0)
-    assert str(xval.dtype) == 'uint8'
-    assert xval.min() >= 0
-    assert xval.max() < 256
-    assert xval.max() > 1
-    assert xval.shape == (1024,)
+class TestCifar10(unittest.TestCase):
 
-    #uint8 rasterized rgb
-    x, y = cifar10(s_idx, split='train', dtype='uint8', rasterized=True, color='rgb')
-    f = pfunc([s_idx], [x,y])
-    xval, yval = f(0)
-    assert str(xval.dtype) == 'uint8'
-    assert xval.min() >= 0
-    assert xval.max() < 256
-    assert xval.max() > 1
-    assert xval.shape == (1024, 3)
-
-    # ranges are handled independently from shapes, so I'll consider the shapes have been
-    # tested above, and now I just look at ranges for floating-point dtypes
-
-    #float32 
-    x, y = cifar10(s_idx, split='train', dtype='float32', rasterized=False, color='grey')
-    f = pfunc([s_idx], [x,y])
-    xval, yval = f(0)
-    assert str(xval.dtype) == 'float32'
-    assert xval.min() >= 0.0
-    assert xval.max() <= 1.0
-    assert xval.max() > 0.01
-    assert xval.shape == (32,32)
+    def setUp(self):
+        pass
 
-    #float64
-    x, y = cifar10(s_idx + range(5), split='train', dtype='float64', rasterized=True, color='rgb')
-    f = pfunc([s_idx], [x,y])
-    xval, yval = f(0)
-    assert str(xval.dtype) == 'float64'
-    assert xval.min() >= 0.0
-    assert xval.max() <= 1.0
-    assert xval.max() > 0.01
-    assert xval.shape == (5, 1024, 3)
-
-def test_split_different():
-    s_idx = theano.tensor.iscalar()
-    x, y = cifar10(s_idx, split='train', dtype='uint8', rasterized=False, color='grey')
-    f = pfunc([s_idx], [x,y])
-    train_xval, train_yval = f(0)
-
-    x, y = cifar10(s_idx, split='valid', dtype='uint8', rasterized=False, color='grey')
-    f = pfunc([s_idx], [x,y])
-    valid_xval, valid_yval = f(0)
-
-    x, y = cifar10(s_idx, split='test', dtype='uint8', rasterized=False, color='grey')
-    f = pfunc([s_idx], [x,y])
-    test_xval, test_yval = f(0)
-
-    assert not numpy.all(train_xval == valid_xval)
-    assert not numpy.all(train_xval == test_xval)
-    assert not numpy.all(valid_xval == test_xval)
+    def tearDown(self):
+        forget()
 
 
-def test_split_length():
-    """test that each split has the correct length"""
-    s_idx = theano.tensor.iscalar()
-    for bsize in [1, 3, 5]:
-        for (split, goodlen) in [('train', 40000), ('valid', 10000), ('test', 10000)]:
-            if bsize == 1:
-                x, y = cifar10(s_idx, split=split, dtype='uint8', rasterized=False, color='grey')
-            else:
-                x, y = cifar10(s_idx*bsize + range(bsize), split=split, dtype='uint8', rasterized=False, color='grey')
+    def test_single(self):
+
+        s_idx = theano.tensor.iscalar()
+
+        for dtype in ('uint8', 'float64', 'float32'):
+            x, y = cifar10(s_idx, split='train', dtype=dtype, rasterized=False, color='grey')
+            assert x.dtype == dtype
+
+    def test_shape_range(self):
+        """Test that the image numbers come out in the right range for various dtypes"""
+        s_idx = theano.tensor.iscalar()
+
+        #uint8 not-rasterized grey
+        x, y = cifar10(s_idx, split='train', dtype='uint8', rasterized=False, color='grey')
+        f = pfunc([s_idx], [x,y])
+        xval, yval = f(0)
+        assert str(xval.dtype) == 'uint8'
+        assert xval.min() >= 0
+        assert xval.max() < 256
+        assert xval.max() > 1
+        assert xval.shape == (32,32)
+
+        #uint8 not-rasterized rgb
+        x, y = cifar10(s_idx, split='train', dtype='uint8', rasterized=False, color='rgb')
+        f = pfunc([s_idx], [x,y])
+        xval, yval = f(0)
+        assert str(xval.dtype) == 'uint8'
+        assert xval.min() >= 0
+        assert xval.max() < 256
+        assert xval.max() > 1
+        assert xval.shape == (32,32, 3)
+
+        #uint8 rasterized grey
+        x, y = cifar10(s_idx, split='train', dtype='uint8', rasterized=True, color='grey')
+        f = pfunc([s_idx], [x,y])
+        xval, yval = f(0)
+        assert str(xval.dtype) == 'uint8'
+        assert xval.min() >= 0
+        assert xval.max() < 256
+        assert xval.max() > 1
+        assert xval.shape == (1024,)
+
+        #uint8 rasterized rgb
+        x, y = cifar10(s_idx, split='train', dtype='uint8', rasterized=True, color='rgb')
+        f = pfunc([s_idx], [x,y])
+        xval, yval = f(0)
+        assert str(xval.dtype) == 'uint8'
+        assert xval.min() >= 0
+        assert xval.max() < 256
+        assert xval.max() > 1
+        assert xval.shape == (1024, 3)
+
+        # ranges are handled independently from shapes, so I'll consider the shapes have been
+        # tested above, and now I just look at ranges for floating-point dtypes
 
-            f = pfunc([s_idx], [x,y])
-            i = 0
-            while i < 900000:
-                try:
-                    f(i)
-                except IndexError:
-                    break
-                i += 1
-            assert i == (goodlen / bsize) # when goodlen % bsize, we should skip the extra bit
+        #float32 
+        x, y = cifar10(s_idx, split='train', dtype='float32', rasterized=False, color='grey')
+        f = pfunc([s_idx], [x,y])
+        xval, yval = f(0)
+        assert str(xval.dtype) == 'float32'
+        assert xval.min() >= 0.0
+        assert xval.max() <= 1.0
+        assert xval.max() > 0.01
+        assert xval.shape == (32,32)
+
+        #float64
+        x, y = cifar10(s_idx + range(5), split='train', dtype='float64', rasterized=True, color='rgb')
+        f = pfunc([s_idx], [x,y])
+        xval, yval = f(0)
+        assert str(xval.dtype) == 'float64'
+        assert xval.min() >= 0.0
+        assert xval.max() <= 1.0
+        assert xval.max() > 0.01
+        assert xval.shape == (5, 1024, 3)
+
+    def test_split_different(self):
+        s_idx = theano.tensor.iscalar()
+        x, y = cifar10(s_idx, split='train', dtype='uint8', rasterized=False, color='grey')
+        f = pfunc([s_idx], [x,y])
+        train_xval, train_yval = f(0)
+
+        x, y = cifar10(s_idx, split='valid', dtype='uint8', rasterized=False, color='grey')
+        f = pfunc([s_idx], [x,y])
+        valid_xval, valid_yval = f(0)
 
+        x, y = cifar10(s_idx, split='test', dtype='uint8', rasterized=False, color='grey')
+        f = pfunc([s_idx], [x,y])
+        test_xval, test_yval = f(0)
+
+        assert not numpy.all(train_xval == valid_xval)
+        assert not numpy.all(train_xval == test_xval)
+        assert not numpy.all(valid_xval == test_xval)
+
+
+    def test_split_length(self):
+        """test that each split has the correct length"""
+        s_idx = theano.tensor.iscalar()
+        for bsize in [1, 3, 5]:
+            for (split, goodlen) in [('train', 40000), ('valid', 10000), ('test', 10000)]:
+                if bsize == 1:
+                    x, y = cifar10(s_idx, split=split, dtype='uint8', rasterized=False, color='grey')
+                else:
+                    x, y = cifar10(s_idx*bsize + range(bsize), split=split, dtype='uint8', rasterized=False, color='grey')
+
+                f = pfunc([s_idx], [x,y])
+                i = 0
+                while i < 900000:
+                    try:
+                        f(i)
+                    except IndexError:
+                        break
+                    i += 1
+                assert i == (goodlen / bsize) # when goodlen % bsize, we should skip the extra bit
+